365 citations · 463 across the 8 of their papers we have counts for
11 papers
Compressed Error HARQ: Feedback Communication on Noise-Asymmetric Channels
Sravan Kumar Ankireddy, S. Ashwin Hebbar, Yihan Jiang +2
In modern communication systems with feedback, there are increasingly more scenarios where the transmitter has much less power than the receiver (e.g., medical implant devices), wh…
Turbo Autoencoder with a Trainable Interleaver
Karl Chahine, Yihan Jiang, Pooja Nuti +2
A critical aspect of reliable communication involves the design of codes that allow transmissions to be robustly and computationally efficiently decoded under noisy conditions. Adv…
Deepcode and Modulo-SK are Designed for Different Settings
Hyeji Kim, Yihan Jiang, Sreeram Kannan +2
We respond to [1] which claimed that "Modulo-SK scheme outperforms Deepcode [2]". We demonstrate that this statement is not true: the two schemes are designed and evaluated for ent…
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Designing codes that combat the noise in a communication medium has remained a significant area of research in information theory as well as wireless communications. Asymptotically…
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang, Jakub Konečný, Keith Rush +1
Federated Learning (FL) refers to learning a high quality global model based on decentralized data storage, without ever copying the raw data. A natural scenario arises with data c…
DeepTurbo: Deep Turbo Decoder
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Present-day communication systems routinely use codes that approach the channel capacity when coupled with a computationally efficient decoder. However, the decoder is typically de…
MIND: Model Independent Neural Decoder
Yihan Jiang, Hyeji Kim, Himanshu Asnani +1
Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismat…
BOAssembler: a Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
Shunfu Mao, Yihan Jiang, Edwin Basil Mathew +1
High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts fro…
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Yihan Jiang, Hyeji Kim, Himanshu Asnani +3
Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional…
Deepcode: Feedback Codes via Deep Learning
Hyeji Kim, Yihan Jiang, Sreeram Kannan +2
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical ap…
Communication Algorithms via Deep Learning
Hyeji Kim, Yihan Jiang, Ranvir Rana +3
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by indiv…